How DEM Optimizes Drum Granulation

Imagine inside a rotating drum, thousands of fertilizer particles are undergoing complex collisions, mixing, and agglomeration. This process determines the final size, shape, and strength of fertilizer granules, directly impacting fertilizer quality and application effectiveness. However, this dynamic process occurs inside enclosed equipment, invisible to the naked eye. Traditional experimental methods can only see the final result, making it difficult to understand what happens in between. The Discrete Element Method (DEM) acts like a "digital microscope," capable of precisely simulating the motion trajectory and collision process of each particle, allowing researchers to "see" the microscopic world of granulation, thereby optimizing equipment design and process parameters. I. What is the Discrete Element Method? A Digital Laboratory for Granulation Research The Discrete Element Method is a numerical simulation method based on Newton's second law of motion. It treats each particle inside the granulator as an independent individual, calculating the forces acting on each particle (gravity, friction, collision force, cohesive force, etc.), then determines the particle's position and velocity at the next moment based on these forces. By tracking changes every microsecond for millions or even billions of particles, DEM can reconstruct the dynamic scenario of the entire granulation process. For studying drum granulators, a DEM model needs to include three key components: first, the equipment model—accurately replicating the drum's dimensions, inclination angle, and internal lifter structure; second, the particle model—defining physical properties like particle size, density, and friction coefficients for seed cores and coating powders; and finally, the bonding model—simulating how binders "glue" particles together to form agglomerates. This digital laboratory can repeatedly conduct "virtual experiments," changing parameters like rotation speed, inclination angle, and feed rate to observe how these changes affect granulation outcomes, without consuming real materials and energy.

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